Experiment

Stimuli

  • Stimuli were selected from the previous study (the sound of teaching music/experiment-1).
  • 48 stimuli were randomly chosen from each technique (either articulation or dynamics - therefore in total 96 stimuli). The half of them was selected from the teaching condition and the other half was from the performing condition.
    • 24 for teaching-articulation performances
    • 24 for performing-articulation performances
    • 24 for teaching-dynamics performances
    • 24 for performing-dynamics performances

Design

  • 2 blocks (articulation / dynamics): each block only contained performances with either of articulation or dynamics. The order of the blocks was counterbalanced across participants.
  • 48 trials for each block (each stimulus was presented only once) and the stimuli were randomly presented within the block.

Procedure

  1. Musicians (> 6yo experience) were asked to listen to a number of recordings and judge whether each performance was produced for teaching purposes or not.

Actual instruction:

Each performance was produced in order to either 1) teach the musical expressive technique (as a teacher) or 2) perform their best (as a performer).

You will be asked to judge whether each performer had the intention to teach or not by pressing the 'Yes' <Left> or 'No' <Right> key.
  1. Participants pressed “YES” if they think the recording was produced for teaching (as a teacher) whereas they pressed “NO” if they thought the recording was produced for performing (as a performer).

Variables

  • IOIs (tempo)
  • IOIs at transition points (tempo)
  • CV (tempo)
  • KOT (articulation)
  • KV (dynamics)
  • KV-Diff (dynamics)

Judged as “teaching” (%) for each stimulus

Results

1. Correlations

IOIs

All

## `geom_smooth()` using formula 'y ~ x'

Articulation (***)

# normality check
ioi_art_norm_teaching <- shapiro.test(ioi[Skill == "articulation"]$Teaching)
ioi_art_norm_judge <- shapiro.test(ioi[Skill == "articulation"]$Mean)

qqnorm(ioi[Skill == "articulation"]$Teaching)
qqline(ioi[Skill == "articulation"]$Teaching)

ioi_art_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(ioi[Skill == "articulation"]$Mean)
qqline(ioi[Skill == "articulation"]$Mean)

ioi_art_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi[Skill == "articulation"]$Mean
## W = 0.98238, p-value = 0.6803
cor_ioi_art <- cor.test(ioi[Skill == "articulation"]$Teaching, ioi[Skill == "articulation"]$Mean)
cor_ioi_art
## 
##  Pearson's product-moment correlation
## 
## data:  ioi[Skill == "articulation"]$Teaching and ioi[Skill == "articulation"]$Mean
## t = 8.2739, df = 46, p-value = 1.171e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6270180 0.8669915
## sample estimates:
##       cor 
## 0.7733708

Dynamics (**)

# normality check
ioi_dyn_norm_teaching <- shapiro.test(ioi[Skill == "dynamics"]$Teaching)
ioi_dyn_norm_judge <- shapiro.test(ioi[Skill == "dynamics"]$Mean)

qqnorm(ioi[Skill == "dynamics"]$Teaching)
qqline(ioi[Skill == "dynamics"]$Teaching)

ioi_dyn_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(ioi[Skill == "dynamics"]$Mean)
qqline(ioi[Skill == "dynamics"]$Mean)

ioi_dyn_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi[Skill == "dynamics"]$Mean
## W = 0.94923, p-value = 0.03727
cor_ioi_dyn <- cor.test(ioi[Skill == "dynamics"]$Teaching, ioi[Skill == "dynamics"]$Mean)
cor_ioi_dyn
## 
##  Pearson's product-moment correlation
## 
## data:  ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## t = 3.151, df = 46, p-value = 0.00286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1558612 0.6300448
## sample estimates:
##       cor 
## 0.4213368
cor_ioi_dyn_spearman <- cor.test(ioi[Skill == "dynamics"]$Teaching, ioi[Skill == "dynamics"]$Mean, method = "spearman", exact = FALSE)
cor_ioi_dyn_spearman
## 
##  Spearman's rank correlation rho
## 
## data:  ioi[Skill == "dynamics"]$Teaching and ioi[Skill == "dynamics"]$Mean
## S = 13487, p-value = 0.06555
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2679796

Tempo at transition points

All

## `geom_smooth()` using formula 'y ~ x'

Articulation (***)

# normality check
ioi_art_tra_norm_teaching <- shapiro.test(ioi_tra[Skill == "articulation"]$Teaching)
ioi_art_tra_norm_judge <- shapiro.test(ioi_tra[Skill == "articulation"]$Mean)

qqnorm(ioi_tra[Skill == "articulation"]$Teaching)
qqline(ioi_tra[Skill == "articulation"]$Teaching)

ioi_art_tra_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi_tra[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(ioi_tra[Skill == "articulation"]$Mean)
qqline(ioi_tra[Skill == "articulation"]$Mean)

ioi_art_tra_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi_tra[Skill == "articulation"]$Mean
## W = 0.9642, p-value = 0.1492
cor.test(ioi_tra[Skill == "articulation"]$Teaching, ioi_tra[Skill == "articulation"]$Mean)
## 
##  Pearson's product-moment correlation
## 
## data:  ioi_tra[Skill == "articulation"]$Teaching and ioi_tra[Skill == "articulation"]$Mean
## t = 6.4766, df = 46, p-value = 5.572e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5057195 0.8148547
## sample estimates:
##       cor 
## 0.6906176

Dynamics (**)

# normality check
ioi_dyn_tra_norm_teaching <- shapiro.test(ioi_tra[Skill == "dynamics"]$Teaching)
ioi_dyn_tra_norm_judge <- shapiro.test(ioi_tra[Skill == "dynamics"]$Mean)

qqnorm(ioi_tra[Skill == "dynamics"]$Teaching)
qqline(ioi_tra[Skill == "dynamics"]$Teaching)

ioi_dyn_tra_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi_tra[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(ioi_tra[Skill == "dynamics"]$Mean)
qqline(ioi_tra[Skill == "dynamics"]$Mean)

ioi_dyn_tra_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  ioi_tra[Skill == "dynamics"]$Mean
## W = 0.884, p-value = 0.0001991
cor.test(ioi_tra[Skill == "dynamics"]$Teaching, ioi_tra[Skill == "dynamics"]$Mean)
## 
##  Pearson's product-moment correlation
## 
## data:  ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## t = 3.1131, df = 46, p-value = 0.003181
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1509028 0.6269728
## sample estimates:
##       cor 
## 0.4171514
cor.test(ioi_tra[Skill == "dynamics"]$Teaching, ioi_tra[Skill == "dynamics"]$Mean, method = "spearman", exact = FALSE)
## 
##  Spearman's rank correlation rho
## 
## data:  ioi_tra[Skill == "dynamics"]$Teaching and ioi_tra[Skill == "dynamics"]$Mean
## S = 10569, p-value = 0.002514
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.4263485

CV (tempo variability)

All

## `geom_smooth()` using formula 'y ~ x'

## Articulation (n.s.)

# normality check
cv_art_norm_teaching <- shapiro.test(cv[Skill == "articulation"]$Teaching)
cv_art_norm_judge <- shapiro.test(cv[Skill == "articulation"]$CV)

qqnorm(cv[Skill == "articulation"]$Teaching)
qqline(cv[Skill == "articulation"]$Teaching)

cv_art_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  cv[Skill == "articulation"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(cv[Skill == "articulation"]$CV)
qqline(cv[Skill == "articulation"]$CV)

cv_art_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  cv[Skill == "articulation"]$CV
## W = 0.6622, p-value = 3.027e-09
cor.test(cv[Skill == "articulation"]$Teaching, cv[Skill == "articulation"]$CV)
## 
##  Pearson's product-moment correlation
## 
## data:  cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## t = -1.7368, df = 46, p-value = 0.08912
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.49716450  0.03879628
## sample estimates:
##       cor 
## -0.248073
cor.test(cv[Skill == "articulation"]$Teaching, cv[Skill == "articulation"]$CV, method = "spearman", exact = FALSE)
## 
##  Spearman's rank correlation rho
## 
## data:  cv[Skill == "articulation"]$Teaching and cv[Skill == "articulation"]$CV
## S = 23645, p-value = 0.05096
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##      rho 
## -0.28338

Dynamics (n.s.)

# normality check
cv_dyn_norm_teaching <- shapiro.test(cv[Skill == "dynamics"]$Teaching)
cv_dyn_norm_judge <- shapiro.test(cv[Skill == "dynamics"]$CV)

qqnorm(cv[Skill == "dynamics"]$Teaching)
qqline(cv[Skill == "dynamics"]$Teaching)

cv_dyn_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  cv[Skill == "dynamics"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(cv[Skill == "dynamics"]$CV)
qqline(cv[Skill == "dynamics"]$CV)

cv_dyn_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  cv[Skill == "dynamics"]$CV
## W = 0.86123, p-value = 4.381e-05
cor.test(cv[Skill == "dynamics"]$Teaching, cv[Skill == "dynamics"]$CV)
## 
##  Pearson's product-moment correlation
## 
## data:  cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## t = -0.53753, df = 46, p-value = 0.5935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3551678  0.2098392
## sample estimates:
##         cor 
## -0.07900602
cor.test(cv[Skill == "dynamics"]$Teaching, cv[Skill == "dynamics"]$CV, method = "spearman", exact = FALSE)
## 
##  Spearman's rank correlation rho
## 
## data:  cv[Skill == "dynamics"]$Teaching and cv[Skill == "dynamics"]$CV
## S = 19384, p-value = 0.7252
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.05208142

KOT

All

## `geom_smooth()` using formula 'y ~ x'

Legato (**)

# normality check
kot_leg_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Legato"]$Teaching)
kot_leg_norm_judge <- shapiro.test(kot_all[Subcomponent == "Legato"]$Mean)

qqnorm(kot_all[Subcomponent == "Legato"]$Teaching)
qqline(kot_all[Subcomponent == "Legato"]$Teaching)

kot_leg_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Legato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(kot_all[Subcomponent == "Legato"]$Mean)
qqline(kot_all[Subcomponent == "Legato"]$Mean)

kot_leg_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Legato"]$Mean
## W = 0.97678, p-value = 0.4526
cor_kot_leg <- cor.test(kot_all[Subcomponent == "Legato"]$Teaching, kot_all[Subcomponent == "Legato"]$Mean)
cor_kot_leg
## 
##  Pearson's product-moment correlation
## 
## data:  kot_all[Subcomponent == "Legato"]$Teaching and kot_all[Subcomponent == "Legato"]$Mean
## t = 2.9349, df = 46, p-value = 0.005192
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1273759 0.6121920
## sample estimates:
##       cor 
## 0.3971374

Staccato (***)

# normality check
kot_sta_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Staccato"]$Teaching)
kot_sta_norm_judge <- shapiro.test(kot_all[Subcomponent == "Staccato"]$Mean)

qqnorm(kot_all[Subcomponent == "Staccato"]$Teaching)
qqline(kot_all[Subcomponent == "Staccato"]$Teaching)

kot_sta_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Staccato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(kot_all[Subcomponent == "Staccato"]$Mean)
qqline(kot_all[Subcomponent == "Staccato"]$Mean)

kot_sta_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Staccato"]$Mean
## W = 0.95441, p-value = 0.06005
cor_kot_sta <- cor.test(kot_all[Subcomponent == "Staccato"]$Teaching, kot_all[Subcomponent == "Staccato"]$Mean)
cor_kot_sta
## 
##  Pearson's product-moment correlation
## 
## data:  kot_all[Subcomponent == "Staccato"]$Teaching and kot_all[Subcomponent == "Staccato"]$Mean
## t = -7.3302, df = 46, p-value = 2.919e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8424434 -0.5684184
## sample estimates:
##        cor 
## -0.7340057

Forte (n.s.)

# normality check
kot_for_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Forte"]$Teaching)
kot_for_norm_judge <- shapiro.test(kot_all[Subcomponent == "Forte"]$Mean)

qqnorm(kot_all[Subcomponent == "Forte"]$Teaching)
qqline(kot_all[Subcomponent == "Forte"]$Teaching)

kot_for_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Forte"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(kot_all[Subcomponent == "Forte"]$Mean)
qqline(kot_all[Subcomponent == "Forte"]$Mean)

kot_for_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Forte"]$Mean
## W = 0.76648, p-value = 2.454e-07
cor_kot_for <- cor.test(kot_all[Subcomponent == "Forte"]$Teaching, kot_all[Subcomponent == "Forte"]$Mean)
cor_kot_for
## 
##  Pearson's product-moment correlation
## 
## data:  kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## t = -0.24099, df = 46, p-value = 0.8106
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3164515  0.2511592
## sample estimates:
##         cor 
## -0.03550962
cor_kot_for_spearman <- cor.test(kot_all[Subcomponent == "Forte"]$Teaching, kot_all[Subcomponent == "Forte"]$Mean, method = "spearman", exact = FALSE)
cor_kot_for_spearman
## 
##  Spearman's rank correlation rho
## 
## data:  kot_all[Subcomponent == "Forte"]$Teaching and kot_all[Subcomponent == "Forte"]$Mean
## S = 15680, p-value = 0.3123
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1489441

Piano (n.s.)

# normality check
kot_pia_norm_teaching <- shapiro.test(kot_all[Subcomponent == "Piano"]$Teaching)
kot_pia_norm_judge <- shapiro.test(kot_all[Subcomponent == "Piano"]$Mean)

qqnorm(kot_all[Subcomponent == "Piano"]$Teaching)
qqline(kot_all[Subcomponent == "Piano"]$Teaching)

kot_pia_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Piano"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(kot_all[Subcomponent == "Piano"]$Mean)
qqline(kot_all[Subcomponent == "Piano"]$Mean)

kot_pia_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  kot_all[Subcomponent == "Piano"]$Mean
## W = 0.71858, p-value = 2.869e-08
cor_kot_pia <- cor.test(kot_all[Subcomponent == "Piano"]$Teaching, kot_all[Subcomponent == "Piano"]$Mean)
cor_kot_pia
## 
##  Pearson's product-moment correlation
## 
## data:  kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## t = -1.3414, df = 46, p-value = 0.1864
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.45317378  0.09537053
## sample estimates:
##        cor 
## -0.1940218
cor_kot_pia_spearman <- cor.test(kot_all[Subcomponent == "Piano"]$Teaching, kot_all[Subcomponent == "Piano"]$Mean, method = "spearman", exact = FALSE)
cor_kot_pia_spearman
## 
##  Spearman's rank correlation rho
## 
## data:  kot_all[Subcomponent == "Piano"]$Teaching and kot_all[Subcomponent == "Piano"]$Mean
## S = 20595, p-value = 0.4251
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.1178369

KV

All

## `geom_smooth()` using formula 'y ~ x'

Forte (***)

# normality check
vel_for_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Forte"]$Teaching)
vel_for_norm_judge <- shapiro.test(vel_all[Subcomponent == "Forte"]$Mean)

qqnorm(vel_all[Subcomponent == "Forte"]$Teaching)
qqline(vel_all[Subcomponent == "Forte"]$Teaching)

vel_for_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Forte"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_all[Subcomponent == "Forte"]$Mean)
qqline(vel_all[Subcomponent == "Forte"]$Mean)

vel_for_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Forte"]$Mean
## W = 0.97045, p-value = 0.2636
cor_vel_for <- cor.test(vel_all[Subcomponent == "Forte"]$Teaching, vel_all[Subcomponent == "Forte"]$Mean)
cor_vel_for
## 
##  Pearson's product-moment correlation
## 
## data:  vel_all[Subcomponent == "Forte"]$Teaching and vel_all[Subcomponent == "Forte"]$Mean
## t = 3.439, df = 46, p-value = 0.001251
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1928879 0.6525249
## sample estimates:
##       cor 
## 0.4522372

Piano (n.s.)

# normality check
vel_pia_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Piano"]$Teaching)
vel_pia_norm_judge <- shapiro.test(vel_all[Subcomponent == "Piano"]$Mean)

qqnorm(vel_all[Subcomponent == "Piano"]$Teaching)
qqline(vel_all[Subcomponent == "Piano"]$Teaching)

vel_pia_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Piano"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_all[Subcomponent == "Piano"]$Mean)
qqline(vel_all[Subcomponent == "Piano"]$Mean)

vel_pia_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Piano"]$Mean
## W = 0.98827, p-value = 0.9089
cor_vel_pia <- cor.test(vel_all[Subcomponent == "Piano"]$Teaching, vel_all[Subcomponent == "Piano"]$Mean)
cor_vel_pia
## 
##  Pearson's product-moment correlation
## 
## data:  vel_all[Subcomponent == "Piano"]$Teaching and vel_all[Subcomponent == "Piano"]$Mean
## t = -1.5371, df = 46, p-value = 0.1311
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.47531585  0.06733088
## sample estimates:
##        cor 
## -0.2210324

Legato (n.s.)

# normality check
vel_leg_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Legato"]$Teaching)
vel_leg_norm_judge <- shapiro.test(vel_all[Subcomponent == "Legato"]$Mean)

qqnorm(vel_all[Subcomponent == "Legato"]$Teaching)
qqline(vel_all[Subcomponent == "Legato"]$Teaching)

vel_leg_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Legato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_all[Subcomponent == "Legato"]$Mean)
qqline(vel_all[Subcomponent == "Legato"]$Mean)

vel_leg_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Legato"]$Mean
## W = 0.91277, p-value = 0.001668
cor_vel_leg <- cor.test(vel_all[Subcomponent == "Legato"]$Teaching, vel_all[Subcomponent == "Legato"]$Mean)
cor_vel_leg
## 
##  Pearson's product-moment correlation
## 
## data:  vel_all[Subcomponent == "Legato"]$Teaching and vel_all[Subcomponent == "Legato"]$Mean
## t = 0.65532, df = 46, p-value = 0.5155
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1932420  0.3701921
## sample estimates:
##       cor 
## 0.0961733
cor_vel_leg_spearman <- cor.test(vel_all[Subcomponent == "Legato"]$Teaching, vel_all[Subcomponent == "Legato"]$Mean, method = "spearman", exact = FALSE)
cor_vel_leg_spearman
## 
##  Spearman's rank correlation rho
## 
## data:  vel_all[Subcomponent == "Legato"]$Teaching and vel_all[Subcomponent == "Legato"]$Mean
## S = 17876, p-value = 0.8409
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.02975708

Staccato (n.s.)

# normality check
vel_sta_norm_teaching <- shapiro.test(vel_all[Subcomponent == "Staccato"]$Teaching)
vel_sta_norm_judge <- shapiro.test(vel_all[Subcomponent == "Staccato"]$Mean)

qqnorm(vel_all[Subcomponent == "Staccato"]$Teaching)
qqline(vel_all[Subcomponent == "Staccato"]$Teaching)

vel_sta_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Staccato"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_all[Subcomponent == "Staccato"]$Mean)
qqline(vel_all[Subcomponent == "Staccato"]$Mean)

vel_sta_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_all[Subcomponent == "Staccato"]$Mean
## W = 0.97486, p-value = 0.3867
cor_vel_sta <- cor.test(vel_all[Subcomponent == "Staccato"]$Teaching, vel_all[Subcomponent == "Staccato"]$Mean)
cor_vel_sta
## 
##  Pearson's product-moment correlation
## 
## data:  vel_all[Subcomponent == "Staccato"]$Teaching and vel_all[Subcomponent == "Staccato"]$Mean
## t = -0.15953, df = 46, p-value = 0.8739
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3056081  0.2623724
## sample estimates:
##         cor 
## -0.02351532

KV-Diff

All

## `geom_smooth()` using formula 'y ~ x'

Forte to Piano (***)

# normality check
vel_diff_ftop_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "FtoP"]$Teaching)
vel_diff_ftop_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "FtoP"]$Mean)

qqnorm(vel_diff_all[Subcomponent == "FtoP"]$Teaching)
qqline(vel_diff_all[Subcomponent == "FtoP"]$Teaching)

vel_diff_ftop_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "FtoP"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_diff_all[Subcomponent == "FtoP"]$Mean)
qqline(vel_diff_all[Subcomponent == "FtoP"]$Mean)

vel_diff_ftop_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "FtoP"]$Mean
## W = 0.97502, p-value = 0.3919
cor_vel_diff_ftop <- cor.test(vel_diff_all[Subcomponent == "FtoP"]$Teaching, vel_diff_all[Subcomponent == "FtoP"]$Mean)
cor_vel_diff_ftop
## 
##  Pearson's product-moment correlation
## 
## data:  vel_diff_all[Subcomponent == "FtoP"]$Teaching and vel_diff_all[Subcomponent == "FtoP"]$Mean
## t = -3.8654, df = 46, p-value = 0.0003463
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6831689 -0.2455614
## sample estimates:
##        cor 
## -0.4951484

Piano to Forte (***)

# normality check
vel_diff_ptof_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "PtoF"]$Teaching)
vel_diff_ptof_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "PtoF"]$Mean)

qqnorm(vel_diff_all[Subcomponent == "PtoF"]$Teaching)
qqline(vel_diff_all[Subcomponent == "PtoF"]$Teaching)

vel_diff_ptof_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "PtoF"]$Teaching
## W = 0.96851, p-value = 0.2215
qqnorm(vel_diff_all[Subcomponent == "PtoF"]$Mean)
qqline(vel_diff_all[Subcomponent == "PtoF"]$Mean)

vel_diff_ptof_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "PtoF"]$Mean
## W = 0.97646, p-value = 0.4411
cor_vel_diff_ptof <- cor.test(vel_diff_all[Subcomponent == "PtoF"]$Teaching, vel_diff_all[Subcomponent == "PtoF"]$Mean)
cor_vel_diff_ptof
## 
##  Pearson's product-moment correlation
## 
## data:  vel_diff_all[Subcomponent == "PtoF"]$Teaching and vel_diff_all[Subcomponent == "PtoF"]$Mean
## t = 5.3289, df = 46, p-value = 2.893e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4047327 0.7672651
## sample estimates:
##       cor 
## 0.6178191

Legato to Staccato (n.s.)

# normality check
vel_diff_ltos_norm_teaching <- shapiro.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching)
vel_diff_ltos_norm_judge <- shapiro.test(vel_diff_all[Subcomponent == "LtoS"]$Mean)

qqnorm(vel_diff_all[Subcomponent == "LtoS"]$Teaching)
qqline(vel_diff_all[Subcomponent == "LtoS"]$Teaching)

vel_diff_ltos_norm_teaching
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "LtoS"]$Teaching
## W = 0.9715, p-value = 0.2895
qqnorm(vel_diff_all[Subcomponent == "LtoS"]$Mean)
qqline(vel_diff_all[Subcomponent == "LtoS"]$Mean)

vel_diff_ltos_norm_judge
## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "LtoS"]$Mean
## W = 0.93052, p-value = 0.007129
cor_vel_diff_ltos <- cor.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching, vel_diff_all[Subcomponent == "LtoS"]$Mean)
cor_vel_diff_ltos
## 
##  Pearson's product-moment correlation
## 
## data:  vel_diff_all[Subcomponent == "LtoS"]$Teaching and vel_diff_all[Subcomponent == "LtoS"]$Mean
## t = -1.8478, df = 46, p-value = 0.07107
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.50897866  0.02299395
## sample estimates:
##        cor 
## -0.2628581
cor_vel_diff_ltos_spearman <- cor.test(vel_diff_all[Subcomponent == "LtoS"]$Teaching, vel_diff_all[Subcomponent == "LtoS"]$Mean, method = "spearman", exact = FALSE)
cor_vel_diff_ltos_spearman
## 
##  Spearman's rank correlation rho
## 
## data:  vel_diff_all[Subcomponent == "LtoS"]$Teaching and vel_diff_all[Subcomponent == "LtoS"]$Mean
## S = 22664, p-value = 0.1156
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.2301161

Staccato to Legato (n.s.)

## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "StoL"]$Teaching
## W = 0.9715, p-value = 0.2895

## 
##  Shapiro-Wilk normality test
## 
## data:  vel_diff_all[Subcomponent == "StoL"]$Mean
## W = 0.9637, p-value = 0.1423
## 
##  Pearson's product-moment correlation
## 
## data:  vel_diff_all[Subcomponent == "StoL"]$Teaching and vel_diff_all[Subcomponent == "StoL"]$Mean
## t = -1.28, df = 46, p-value = 0.207
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4460800  0.1041734
## sample estimates:
##        cor 
## -0.1854505

2. Partical correlation (only for KOT, KV, KV-Diff)

KOT

Legato (**)

Estimate

  • partial correlation coefficient between two variables (Teaching vs KOT)

pcor.test(partial[Subcomponent == "Legato"]$KOT, partial[Subcomponent == "Legato"]$Teaching, partial[Subcomponent == "Legato", c("IOI", "KV", "KVDiff")])

Staccato (*)

Estimate

  • partial correlation coefficient between two variables (Teaching vs KOT)

pcor.test(partial[Subcomponent == "Staccato"]$KOT, partial[Subcomponent == "Staccato"]$Teaching, partial[Subcomponent == "Staccato", c("IOI", "KV", "KVDiff")])

KV

Forte (*)

Estimate

  • partial correlation coefficient between two variables (Teaching vs KV)

pcor.test(partial[Subcomponent == "Forte"]$KV, partial[Subcomponent == "Forte"]$Teaching, partial[Subcomponent == "Forte", c("IOI", "KOT", "KVDiff")])

Piano (n.s.)

Estimate

  • partial correlation coefficient between two variables (Teaching vs KV)

pcor.test(partial[Subcomponent == "Piano"]$KV, partial[Subcomponent == "Piano"]$Teaching, partial[Subcomponent == "Piano", c("IOI", "KOT", "KVDiff")])

KV-Diff

Forte to Piano (n.s.)

Estimate

  • partial correlation coefficient between two variables (Teaching vs KVDiff)

pcor.test(partial[Subcomponent2 == "FtoP"]$KVDiff, partial[Subcomponent2 == "FtoP"]$Teaching, partial[Subcomponent2 == "FtoP", c("IOI", "KOT", "KV")])

Piano to Forte (***)

Estimate

  • partial correlation coefficient between two (Teaching vs KVDiff)

pcor.test(partial[Subcomponent2 == "PtoF"]$KVDiff, partial[Subcomponent2 == "PtoF"]$Teaching, partial[Subcomponent2 == "PtoF", c("IOI", "KOT", "KV")])

Multiple regression

Note: additive - no interaction considered

Legato

m1 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Legato"])
summary(m1)
## 
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == 
##     "Legato"])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.2033  -6.7841  -0.8213   7.4083  17.3467 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -84.01813   25.74162  -3.264  0.00216 ** 
## IOI           0.77703    0.09796   7.932 5.92e-10 ***
## KOT           0.25905    0.08430   3.073  0.00367 ** 
## KV           -0.16055    0.23894  -0.672  0.50523    
## KVDiff       -0.08689    0.23141  -0.375  0.70916    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.77 on 43 degrees of freedom
## Multiple R-squared:  0.6764, Adjusted R-squared:  0.6463 
## F-statistic: 22.47 on 4 and 43 DF,  p-value: 4.541e-10
check_model(m1)

Staccato

m2 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Staccato"])
summary(m2)
## 
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == 
##     "Staccato"])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.2526  -7.8795   0.0325   6.8427  26.1285 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -88.1667    23.4668  -3.757 0.000513 ***
## IOI           0.5216     0.1596   3.268 0.002136 ** 
## KOT          -0.2754     0.1137  -2.422 0.019734 *  
## KV            0.1423     0.1983   0.718 0.476815    
## KVDiff       -0.5957     0.2977  -2.001 0.051718 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10.87 on 43 degrees of freedom
## Multiple R-squared:  0.6703, Adjusted R-squared:  0.6397 
## F-statistic: 21.86 on 4 and 43 DF,  p-value: 6.706e-10
check_model(m2)

Forte

m3 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Forte"])
summary(m3)
## 
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == 
##     "Forte"])
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -33.635  -9.911  -0.307   8.468  35.214 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -83.25224   36.29071  -2.294  0.02674 * 
## IOI           0.34645    0.12283   2.821  0.00722 **
## KOT          -0.05319    0.06448  -0.825  0.41398   
## KV            0.72644    0.35607   2.040  0.04751 * 
## KVDiff       -0.60640    0.30913  -1.962  0.05630 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.84 on 43 degrees of freedom
## Multiple R-squared:  0.4136, Adjusted R-squared:  0.359 
## F-statistic: 7.582 on 4 and 43 DF,  p-value: 0.0001027
check_model(m3)

Piano

m4 <- lm(Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == "Piano"])
summary(m4)
## 
## Call:
## lm(formula = Teaching ~ IOI + KOT + KV + KVDiff, data = partial[Subcomponent == 
##     "Piano"])
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -23.7853  -9.3690   0.0482   6.4645  24.9311 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -6.70422   27.71307  -0.242 0.809996    
## IOI          0.38444    0.10726   3.584 0.000857 ***
## KOT         -0.04360    0.06144  -0.710 0.481697    
## KV          -0.67695    0.35915  -1.885 0.066221 .  
## KVDiff       0.98914    0.18738   5.279 4.04e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12.95 on 43 degrees of freedom
## Multiple R-squared:  0.5536, Adjusted R-squared:  0.5121 
## F-statistic: 13.33 on 4 and 43 DF,  p-value: 3.799e-07
check_model(m4)